Homework 1

import dalex as dx
import pandas as pd
import warnings
warnings.filterwarnings('ignore')

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

data = pd.read_csv('hotel_bookings.csv')
data.head()

	hotel	is_canceled	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_weekend_nights	stays_in_week_nights	adults	...	deposit_type	agent	company	days_in_waiting_list	customer_type	adr	required_car_parking_spaces	total_of_special_requests	reservation_status	reservation_status_date
0	Resort Hotel	0	342	2015	July	27	1	0	0	2	...	No Deposit	NaN	NaN	0	Transient	0.0	0	0	Check-Out	2015-07-01
1	Resort Hotel	0	737	2015	July	27	1	0	0	2	...	No Deposit	NaN	NaN	0	Transient	0.0	0	0	Check-Out	2015-07-01
2	Resort Hotel	0	7	2015	July	27	1	0	1	1	...	No Deposit	NaN	NaN	0	Transient	75.0	0	0	Check-Out	2015-07-02
3	Resort Hotel	0	13	2015	July	27	1	0	1	1	...	No Deposit	304.0	NaN	0	Transient	75.0	0	0	Check-Out	2015-07-02
4	Resort Hotel	0	14	2015	July	27	1	0	2	2	...	No Deposit	240.0	NaN	0	Transient	98.0	0	1	Check-Out	2015-07-03

5 rows × 32 columns

# in order to simplify plots I decided to use only a subset of variables
data = data[['is_canceled', 'lead_time', 'arrival_date_year', 'adults', 'children', 'babies', 'booking_changes']]
data = data.dropna()
X, y = data.loc[:, data.columns != 'is_canceled'], data[['is_canceled']]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)

clf = RandomForestClassifier(max_depth=2, random_state=0)
clf.fit(X_train, y_train)

RandomForestClassifier(max_depth=2, random_state=0)

# making prediction on unseen data
observation = X_test.iloc[0,:].to_frame().transpose()
clf.predict(observation)

array([0], dtype=int64)

observation = pd.DataFrame({'lead_time': [203.0],
                       'arrival_date_year': [2016.0],
                       'adults': [2.0],
                       'children': [0.0],
                       'babies': [0.0],
                       'booking_changes': [4.]},
                      index = ['observation'])

exp = dx.Explainer(clf, X_train, y_train)

Preparation of a new explainer is initiated

  -> data              : 107447 rows 6 cols
  -> target variable   : Parameter 'y' was a pandas.DataFrame. Converted to a numpy.ndarray.
  -> target variable   : 107447 values
  -> model_class       : sklearn.ensemble._forest.RandomForestClassifier (default)
  -> label             : Not specified, model's class short name will be used. (default)
  -> predict function  : <function yhat_proba_default at 0x0000019C693170D0> will be used (default)
  -> predict function  : Accepts pandas.DataFrame and numpy.ndarray.
  -> predicted values  : min = 0.139, mean = 0.37, max = 0.45
  -> model type        : classification will be used (default)
  -> residual function : difference between y and yhat (default)
  -> residuals         : min = -0.45, mean = -3.51e-05, max = 0.861
  -> model_info        : package sklearn

A new explainer has been created!

exp.predict(observation)

array([0.3079201])

bd_observation = exp.predict_parts(observation, type='break_down', label=observation.index[0])

bd_observation.result

	variable_name	variable_value	variable	cumulative	contribution	sign	position	label
0	intercept	1	intercept	0.370320	0.370320	1.0	7	observation
1	lead_time	203.0	lead_time = 203.0	0.405468	0.035148	1.0	6	observation
2	adults	2.0	adults = 2.0	0.412069	0.006601	1.0	5	observation
3	babies	0.0	babies = 0.0	0.412456	0.000386	1.0	4	observation
4	children	0.0	children = 0.0	0.412374	-0.000082	-1.0	3	observation
5	arrival_date_year	2016.0	arrival_date_year = 2016.0	0.410609	-0.001765	-1.0	2	observation
6	booking_changes	4.0	booking_changes = 4.0	0.307920	-0.102689	-1.0	1	observation
7			prediction	0.307920	0.307920	1.0	0	observation

bd_observation.plot()
# lead_time variable is equal to number of days between
# entering date and arrival(or cancelation) date

sh_observation = exp.predict_parts(observation, type='shap', B = 10, label=observation.index[0])

sh_observation.plot(bar_width = 16)

# big number of booking changes contributes negatively to final result
# big lead time however makes cancelation more likely

observation1 = pd.DataFrame({'lead_time': [4.0],
                       'arrival_date_year': [2015.0],
                       'adults': [2.0],
                       'children': [0.0],
                       'babies': [1.0],
                       'booking_changes': [0.0]},
                      index = ['observation1'])
sh_observation1 = exp.predict_parts(observation1, type='shap', B = 10, label=observation1.index[0])

sh_observation1.plot(bar_width = 16)
# smaller lead time makes cancelation less likely
# same applies for number of babies
# also absence of booking changes contibutes positively to final result